Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Machine learning is often key to success for today’s institutions that rely heavily on data for success. But often, data science teams can have a difficult time convincing their organizations of the breadth and size of a training data challenge. A new report from Alegion walks through a checklist to review before helping your enterprise take the next step in machine learning.

AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.

AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on. When things go wrong with AI it’s because either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained. Download the new whitepaper from Alegion that can help AI project leads and business sponsors better understand the four distinct types of bias that can affect machine learning, and how each can be mitigated.

Alegion is fully aware of the potential for machine learning bias because as they produce AI training data, the company is on the lookout for biases that can influence machine learning. A new white paper from Alegion, “Four Sources of Machine Learning Bias,” explores the four sources of AI bias, and how to mitigate these challenges for your AI systems.

This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. Artificial intelligence (AI) isn’t perfect. It exists as a combination of algorithms and data; bias can occur in both of these elements. When we produce AI training data, we know to look for biases that can influence machine learning.

As a company that specializes in training AI systems, we know only too well that AI systems do precisely what they are taught to do. Models are only as good as their mathematical construction and the data they are trained on. Algorithms that are biased will end up doing things that reflect that bias.

Industry Perspectives

In this special guest feature, Sean McDermott, CEO and founder of Windward Consulting Group and RedMonocle, offers what enterprises need to know about the five levels of AIOps maturity. When maneuvering through each level, keep the long-term AIOps strategy and goals at the center to achieve the true potential of AIOps.

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White Papers

This white paper by enterprise search specialists Lucidworks, discusses how data is eating the world and search is the key to finding the data you need. The enterprise search industry is consolidating and moving to technologies built around Lucene and Solr. In the next few years we’ll see nearly all search become voice, conversational, and predictive. Search will surround everything we do and the right combination of signal capture, machine learning, and rules are essential to making that work. Fortunately, much of the technology to drive this is available to us today!